IEEE Communications Surveys and Tutorials最新文献

{"title":"A Survey on XAI for 5G and Beyond Security: Technical Aspects, Challenges and Research Directions","authors":"Thulitha Senevirathna;Vinh Hoa La;Samuel Marcha;Bartlomiej Siniarski;Madhusanka Liyanage;Shen Wang","doi":"10.1109/COMST.2024.3437248","DOIUrl":"10.1109/COMST.2024.3437248","url":null,"abstract":"With the advent of 5G commercialization, the need for more reliable, faster, and intelligent telecommunication systems is envisaged for the next generation beyond 5G (B5G) radio access technologies. Artificial Intelligence (AI) and Machine Learning (ML) are immensely popular in service layer applications and have been proposed as essential enablers in many aspects of 5G and beyond networks, from IoT devices and edge computing to cloud-based infrastructures. However, existing 5G ML-based security surveys tend to emphasize AI/ML model performance and accuracy more than the models’ accountability and trustworthiness. In contrast, this paper explores the potential of Explainable AI (XAI) methods, which would allow stakeholders in 5G and beyond to inspect intelligent black-box systems used to secure next-generation networks. The goal of using XAI in the security domain of 5G and beyond is to allow the decision-making processes of ML-based security systems to be transparent and comprehensible to 5G and beyond stakeholders, making the systems accountable for automated actions. In every facet of the forthcoming B5G era, including B5G technologies such as ORAN, zero-touch network management, and end-to-end slicing, this survey emphasizes the role of XAI in them that the general users would ultimately enjoy. Furthermore, we presented the lessons from recent efforts and future research directions on top of the currently conducted projects involving XAI.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"941-973"},"PeriodicalIF":34.4,"publicationDate":"2024-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141880298","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Breaking the Interference and Fading Gridlock in Backscatter Communications: State-of-the-Art, Design Challenges, and Future Directions","authors":"Bowen Gu;Dong Li;Haiyang Ding;Gongpu Wang;Chintha Tellambura","doi":"10.1109/COMST.2024.3436082","DOIUrl":"10.1109/COMST.2024.3436082","url":null,"abstract":"As the Internet of Things (IoT) advances by leaps and bounds, a multitude of devices are becoming interconnected, marking the onset of an era where everything is connected. While this growth opens up opportunities for novel products and applications, it also leads to increased energy reliance on IoT devices, creating a significant bottleneck that hinders sustainable progress. Backscatter communication (BackCom), as a low-power and passive communication technology, emerges as one of the promising solutions to this energy impasse by reducing the manufacturing cost and energy consumption for IoT devices. However, BackCom systems also face some challenges, such as complex interference environments, including the direct-link interference (DLI) and the mutual interference (MI) between tags, which severely disrupt the efficiency of BackCom networks. Moreover, the double-path fading is another major issue that leads to a degraded system performance. To fully unleash the potential of BackComs, the purpose of this paper is to furnish a comprehensive review of existing solutions with a focus on addressing these challenges, offering an insightful analysis and comparison of various strategies. Specifically, we begin by introducing the preliminaries for BackCom, including its history, operating mechanisms, main architectures, etc., providing a foundational understanding of this field. Then, we delve into fundamental issues related to BackCom systems, such as solutions for the DLI, the MI, and the double-path fading. This paper thoroughly provides state-of-the-art advances for each case, particularly highlighting how the latest innovations in theoretical approaches and system design can strategically address these challenges. Finally, we explore emerging trends and challenges in BackComs by forecasting potential technological advancements and providing insights and guidelines for navigating the intricate landscape of future communication needs in a rapidly evolving IoT ecosystem.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"870-911"},"PeriodicalIF":34.4,"publicationDate":"2024-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141862232","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Federated Learning for Urban Sensing Systems: A Comprehensive Survey on Attacks, Defences, Incentive Mechanisms, and Applications","authors":"Ayshika Kapoor;Dheeraj Kumar","doi":"10.1109/COMST.2024.3434510","DOIUrl":"10.1109/COMST.2024.3434510","url":null,"abstract":"In recent years, advancements in Artificial Intelligence (AI), the Internet of Things (IoT) and wireless technologies have propelled the evolution of smart cities. Urban sensing systems collect real-time data from urban areas for various applications, such as environmental monitoring, healthcare, and intelligent transportation, that contribute to the growth of smart cities. In urban sensing, the active participation of users gives rise to participatory sensing, where individuals contribute real-time data through their smartphones or IoT devices, but it encounters bottlenecks in communication, network latency, and user privacy with an exponential rise in data. A prominent characteristic of urban sensing applications is the highly individualized and personal nature of the data, e.g., location and time. Hence, adequate privacy and security provisions are required for these applications to succeed on a high scale. Conventional centralised machine learning approaches expose participants to potential vulnerabilities from malicious tasking servers or inference based on anonymized data. Federated learning (FL) has been proposed as the most viable alternative that leverages the advances in modern-day smartphones’ computation and communication capabilities by allowing participants to train local models on their devices. These models are aggregated by the application server to form a global model without the need for users to share their private data. However, large-scale FL-based urban sensing systems are still not practical due to various challenges associated with their real-life implementation. This paper presents a comprehensive survey addressing practical challenges in implementing FL-based urban sensing applications, e.g., inference attacks, poisoning attacks, and fair incentivization to participants while preserving privacy. We then provide an extensive survey on the use of FL in various urban sensing applications, highlighting that current applications do not simultaneously address all three aforementioned challenges. We conclude this survey by highlighting the research challenges to form a practical FL-based urban sensing system and future research directions.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"1293-1325"},"PeriodicalIF":34.4,"publicationDate":"2024-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141794710","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enabling Intelligent Connectivity: A Survey of Secure ISAC in 6G Networks","authors":"Xiaoqiang Zhu;Jiqiang Liu;Lingyun Lu;Tao Zhang;Tie Qiu;Chunpeng Wang;Yuan Liu","doi":"10.1109/COMST.2024.3432871","DOIUrl":"10.1109/COMST.2024.3432871","url":null,"abstract":"The rapid growth of intelligent sensing capabilities and super computation power in 6G mobile communication systems has facilitated their application in diverse domains such as smart health, smart factories, and the industrial Internet of Things. Integrated Sensing and Communication (ISAC), as a core technology, has merged with artificial intelligence (AI) to enable intelligent connectivity, leading to a paradigm shift in traditional communication modes. This paper presents a visionary design for an ISAC-oriented unified IoT architecture that integrates software-defined communication and super-intelligent agents. By leveraging dynamic adaptability, self-learning, and optimization, the ISAC system can intelligently and flexibly respond to evolving requirements and environments. The architecture is redefined into three layers: the hardware layer, the omniscient layer, and the application layer. Furthermore, a retrospective survey of ISAC technology development over the past decade is conducted, highlighting new design principles for AI-empowered networks and multi-modals that support “intelligent connectivity” across various application scenarios and reinforce the security of ISAC. This paper categorizes the related works according to the different layer structures of the proposed architecture, and some important physical and machine learning models are introduced. Additionally, we summarize the current technological bottlenecks associated with ISAC and propose future research directions and potential solutions that lay the foundation for the future development of secure and intelligent communication networks.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"748-781"},"PeriodicalIF":34.4,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141754849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Intelligent Internet of Things: Applications, Security, Privacy, and Future Directions","authors":"Ons Aouedi;Thai-Hoc Vu;Alessio Sacco;Dinh C. Nguyen;Kandaraj Piamrat;Guido Marchetto;Quoc-Viet Pham","doi":"10.1109/COMST.2024.3430368","DOIUrl":"10.1109/COMST.2024.3430368","url":null,"abstract":"The rapid advances in the Internet of Things (IoT) have promoted a revolution in communication technology and offered various customer services. Artificial intelligence (AI) techniques have been exploited to facilitate IoT operations and maximize their potential in modern application scenarios. In particular, the convergence of IoT and AI has led to a new networking paradigm called Intelligent IoT (IIoT), which has the potential to significantly transform businesses and industrial domains. This paper presents a comprehensive survey of IIoT by investigating its significant applications in mobile networks, as well as its associated security and privacy issues. Specifically, we explore and discuss the roles of IIoT in a wide range of key application domains, from smart healthcare and smart cities to smart transportation and smart industries. Through such extensive discussions, we investigate important security issues in IIoT networks, where network attacks, confidentiality, integrity, and intrusion are analyzed, along with a discussion of potential countermeasures. Privacy issues in IIoT networks were also surveyed and discussed, including data, location, and model privacy leakage. Finally, we outline several key challenges and highlight potential research directions in this important area.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"1238-1292"},"PeriodicalIF":34.4,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10601684","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141725987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Intelligent Solutions for Increased Video Delivery Quality in Cloud–Edge–End Networks","authors":"Wanxin Shi;Qing Li;Qian Yu;Fulin Wang;Gengbiao Shen;Yong Jiang;Yang Xu;Lianbo Ma;Gabriel-Miro Muntean","doi":"10.1109/COMST.2024.3427360","DOIUrl":"10.1109/COMST.2024.3427360","url":null,"abstract":"The digital age has brought a significant increase in video traffic. This traffic growth, driven by rapid Internet advancements and a surge in multimedia applications, presents both challenges and opportunities to video transmissions. Users seek high-quality video content, prompting service providers to offer high-definition options to improve user experience and increase profits. However, traditional end-to-end best-effort networks struggle to meet the demands of extensive video streaming and ensure good user Quality of Experience (QoE), especially in high user mobility scenarios or fluctuating network conditions. Addressing some of these challenges, content delivery networks (CDN) are instrumental in delivering video content, but they are under increased pressure to support high quality and reduce their deployment and maintenance costs. Currently, cloud-edge-end fusion technologies have become one of the optimization directions for network services due to their flexibility and scalability. At the same time, in the context of the recent advancements in computing-focused network paradigms, intelligent enhancement techniques (e.g., super-resolution), commonly utilized in image optimization, have been adopted as a pivotal solution for increasing video delivery quality. To illustrate the essence and employment of the intelligent enhancement solutions for video streaming, this paper first outlines the video streaming process, discusses relevant evaluation metrics, and examines aspects related to the intelligent solutions. Then the paper presents the intelligent enhancement process of video streaming, analyzes various typical intelligent models for content enhancement and highlights their distinct characteristics. This exploration delves deeper into various intelligent quality-improved solutions, scrutinizing their applicability across different transmission scenarios like Video on Demand (VoD) and live streaming, and shedding light on their strengths and weaknesses from a cloud-edge-end fusion perspective. Additionally, the intelligent quality-enhanced video delivery systems are analysed comprehensively, exploring their impact on network traffic, computational demand, and storage needs, and aligning them with potential deployment scenarios and use cases. Finally, the article identifies open issues and key challenges that warrant attention in future research endeavors.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"1363-1394"},"PeriodicalIF":34.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10596127","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Applications of Distributed Machine Learning for the Internet-of-Things: A Comprehensive Survey","authors":"Mai Le;Thien Huynh-The;Tan Do-Duy;Thai-Hoc Vu;Won-Joo Hwang;Quoc-Viet Pham","doi":"10.1109/COMST.2024.3427324","DOIUrl":"10.1109/COMST.2024.3427324","url":null,"abstract":"The emergence of new services and applications in emerging wireless networks (e.g., beyond 5G and 6G) has shown a growing demand for the usage of artificial intelligence (AI) in the Internet of Things (IoTs). However, the proliferation of massive IoT connections and the availability of computing resources distributed across future IoT systems have strongly demanded the development of distributed AI for better IoT services and applications. Therefore, existing AI-enabled IoT systems can be enhanced by implementing distributed machine learning (aka distributed learning) approaches. This work aims to provide a comprehensive survey on distributed learning for IoT services and applications in emerging networks. In particular, we first provide a background of machine learning and present a preliminary to typical distributed learning approaches, such as federated learning, multi-agent reinforcement learning, and distributed inference. Then, we provide an extensive review of distributed learning for critical IoT services (e.g., data sharing and computation offloading, localization, mobile crowdsensing, and security and privacy) and IoT applications (e.g., smart healthcare, smart grid, autonomous vehicle, aerial IoT networks, and smart industry). From the reviewed literature, we also present critical challenges of distributed learning for IoT and propose several promising solutions and research directions in this emerging area.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"1053-1100"},"PeriodicalIF":34.4,"publicationDate":"2024-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141602666","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"UAV-Assisted Communications With RF Energy Harvesting: A Comprehensive Survey","authors":"Gaurav Kumar Pandey;Devendra Singh Gurjar;Suneel Yadav;Yuming Jiang;Chau Yuen","doi":"10.1109/COMST.2024.3425597","DOIUrl":"10.1109/COMST.2024.3425597","url":null,"abstract":"Unmanned aerial vehicles (UAVs) have recently achieved sky-rocketed prominence in assisting various wireless communication scenarios due to their implicit characteristics like line-of-sight connectivity, three-dimensional mobility, on-demand deployment, and payload abilities. For realizing UAV-assisted communications in practical scenarios, one of the key challenges is the onboard power limitation of UAVs, which can affect their flight duration, maneuverability, and network lifetime. Exploiting energy harvesting (EH) techniques with UAV networks can improve operational time by addressing the UAV’s power constraints. In addition, UAVs can facilitate seamless connectivity for energy-constrained low-power devices in remote locations, disaster-struck areas, emergencies, and areas where human intervention is challenging and recharging or replacing batteries is problematic. In such scenarios, a low-altitude UAV, which flies flexibly, can serve as a radio-frequency (RF) energy transmitter to charge the nearby low-power devices efficiently and help enable their communications. Focusing on these aspects, this paper comprehensively surveys the current state-of-the-art EH techniques that can be utilized with UAV communications in various practical scenarios. Then, we highlight crucial challenges in integrating UAVs with prevailing wireless infrastructure and discuss different RF-EH receiver architectures that can be viable solutions for power-constrained UAV networks. Further, we show the channel modeling of UAV-assisted communications with EH and provide critical insights into the system performance. Besides, we offer an exhaustive review of recent works on RF-EH in UAV networks with next-generation paradigms and different problem-solving techniques/optimization methods. Finally, we suggest forthcoming trends and highlight open issues and future research directions.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"782-838"},"PeriodicalIF":34.4,"publicationDate":"2024-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141566208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-Empowered Virtual Network Embedding: A Comprehensive Survey","authors":"Sheng Wu;Ning Chen;Ailing Xiao;Peiying Zhang;Chunxiao Jiang;Wei Zhang","doi":"10.1109/COMST.2024.3424533","DOIUrl":"10.1109/COMST.2024.3424533","url":null,"abstract":"For the challenges posed by Internet rigidity, network virtualization (NV) technology emerges as a pivotal approach, imparting diversity, resilience, and scalability to the evolution of new Internet architecture. By abstraction, allocation, and isolation, the physical network is enabled to host multiple heterogeneous virtual networks (VNs), thereby facilitating the accommodation of user-customized requirements to share physical resources. Nevertheless, a critical challenge in NV implementation is the virtual network embedding (VNE) problem, which concerns the efficient allocation of physical network resources to VNs. In recent years, researchers have increasingly focused on the integration of artificial intelligence (AI) to augment VNE with heightened intelligence, efficiency, dynamics, and interactivity. Therefore, this survey offers a comprehensive overview of AI-empowered VNE algorithms, presenting insights into the general modeling, definition processes, and applications of the fundamental VNE paradigm. Furthermore, an exhaustive taxonomy is presented, encompassing categories such as single-domain/multi-domain, centralized/distributed, online/offline, coordinated/uncoordinated, dynamic/ static, and survivable/unsurvivable. Subsequently, for the prevailing mainstream methods of VNE, reinforcement learning (RL)-based and deep reinforcement learning (DRL)-based, a comprehensive review and comparative analysis of the latest works are conducted within the delineated taxonomy. Finally, the open issues, research challenges, and opportunities for VNE in future settings are identified. In particular, the significant role and key bottlenecks in the urgent vision of satellite-terrestrial integrated networks (STINs) for the 6th generation (6G) communications. This survey is expected to provide comprehensive information, guide scientific research, illuminate frontier trends, and establish the theoretical basis for further research.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"1395-1426"},"PeriodicalIF":34.4,"publicationDate":"2024-07-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141561439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Active Reconfigurable Intelligent Surfaces: Expanding the Frontiers of Wireless Communication-A Survey","authors":"Manzoor Ahmed;Salman Raza;Aized Amin Soofi;Feroz Khan;Wali Ullah Khan;Syed Zain Ul Abideen;Fang Xu;Zhu Han","doi":"10.1109/COMST.2024.3423460","DOIUrl":"10.1109/COMST.2024.3423460","url":null,"abstract":"The swift progress of metasurface technology, enabling meticulous manipulation of the propagation environment, is anticipated to bring a transformative impact on sixth-generation (6G) wireless communications efficiency. Utilizing metasurface elements presents a promising opportunity for achieving passive scattering at sub-wavelength scales, facilitating intelligent radio settings’ advancement. Active Reconfigurable Intelligent Surfaces (ARIS) have gained significant interest in emergent metasurface technology. In contrast to passive RIS, which exhibits a certain degree of performance enhancement but encounters restrictions arising from the “double fading” phenomenon in the phase response, ARIS emerges as a highly promising alternative to counter such restrictions. This study provides a complete examination of ARIS, particularly emphasizing current improvements and its various uses within the context of 6G wireless networks. The review commences by laying a robust foundation in RIS technology, covering the various types and modes of RIS. Following this, we will explore the benefits and practical implementations of ARIS. Through a systematic examination, we categorize different approaches within ARIS-enabled use cases. These scenarios include optimizing the sum rate and signal-to-noise ratio, attaining maximum secrecy rate, energy minimization, and ensuring channel estimation. Additionally, we provide a summary and lessons learned along with a summary table for each category to describe, contrast, and evaluate the existing literature regarding setup, channel characteristics, methodologies, and objectives. We highlight the crucial role of ARIS in defining the landscape of wireless communications in the 6G era by outlining the open research problems in this emerging area and exploring the attractive future prospects.","PeriodicalId":55029,"journal":{"name":"IEEE Communications Surveys and Tutorials","volume":"27 2","pages":"839-869"},"PeriodicalIF":34.4,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141546035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}